Overview

Descriptive Statistics by City

Deployments Overview:
country city year citylabel
Cambodia Siem Reap 2016 Siem Reap 16
Bangladesh Dhaka 2017 Dhaka 17
Ghana Accra 2016 Accra 16
Zambia Lusaka 2018 Lusaka 18
Mozambique Maputo 2015 Maputo 15
Ghana Accra 2018 Accra 18
Ghana Kumasi 2018 Kumasi 18
Mozambique Maputo 2016 Maputo 16
Uganda Kampala 2018 Kampala 18
USA Atlanta 2016 Atlanta 16
India Vellore 2014 Vellore 14
Senegal Dakar 2019 Dakar 19
Zambia Lusaka 2019 Lusaka 19

In this database there are 13 SaniPath deployments.

Number of Neighborhoods

Neighborhoods Overview:
city country year neighb_UID neighborhood
Siem Reap Cambodia 2016 101 Chong Kaosou
Siem Reap Cambodia 2016 102 Kumruthemey (informal)
Siem Reap Cambodia 2016 103 Kumruthemey (formal)
Siem Reap Cambodia 2016 104 Steung Thumey
Siem Reap Cambodia 2016 105 Veal/ Trapangses
Dhaka Bangladesh 2017 201 Kalshi
Dhaka Bangladesh 2017 202 Badda
Dhaka Bangladesh 2017 203 Gabtoli
Dhaka Bangladesh 2017 204 Uttarkhan
Dhaka Bangladesh 2017 205 Gulshan
Dhaka Bangladesh 2017 206 Kamalapur
Dhaka Bangladesh 2017 207 Shampur
Dhaka Bangladesh 2017 208 Hazaribagh
Dhaka Bangladesh 2017 209 Motijhil
Dhaka Bangladesh 2017 210 Dhanmondi
Accra Ghana 2016 301 Shiabu
Accra Ghana 2016 302 Chorkor
Accra Ghana 2016 303 Kokomlemle
Accra Ghana 2016 304 Ringway
Accra Ghana 2016 305 Adabraka
Lusaka Zambia 2018 401 Kanyama
Maputo Mozambique 2015 501 Intervention
Maputo Mozambique 2015 502 Control
Accra Ghana 2018 601 Mataheko
Accra Ghana 2018 602 Osu Alata
Kumasi Ghana 2018 701 FanteNewTown
Kumasi Ghana 2018 702 MoshieZongo
Kumasi Ghana 2018 703 Dakodwom
Kumasi Ghana 2018 704 Ahodwo
Maputo Mozambique 2016 801 Intervention
Maputo Mozambique 2016 802 Control
Kampala Uganda 2018 901 Makindye
Kampala Uganda 2018 902 Central
Kampala Uganda 2018 903 Kawempe
Kampala Uganda 2018 904 Rubaga
Kampala Uganda 2018 905 Nakawa
Atlanta USA 2016 1001 Peoplestown
Vellore India 2014 1101 Old Town
Vellore India 2014 1102 Chinna Allapuram
Dakar Senegal 2019 1201 WakhinaneNimzatt
Dakar Senegal 2019 1202 MedinaGounass
Dakar Senegal 2019 1203 DTK
Dakar Senegal 2019 1204 RufisqueEst
Dakar Senegal 2019 1205 SicapLiberte
Lusaka Zambia 2019 1301 Chawama
Lusaka Zambia 2019 1302 Chazanga
Lusaka Zambia 2019 1303 George

Overall, there are 13 deployments in 9 different countries, 10 cities, and 47 neighborhoods in this analysis.

Number of Samples per Pathway

Pathway Overview:
Accra Atlanta Dakar Dhaka Kampala Kumasi Lusaka Maputo Siem Reap Vellore
Open Drain Water 184 50 100 47 40 50 22
Raw Produce 90 10 50 100 50 39 50 23 33 20
Municipal Drinking Water 88 10 100 100 39 36 40 40 10 22
Ocean 40
Surface Water 100 12 3 10
Floodwater 18 7 100 50 36 30 20 50
Public Latrine 280 10 100 50 27 50 71 24
Soil 108 10 50 100 50 40 50 150 50 20
Bathing Water 9 100 21 50 10 20
Street Food 20 50 100 45 40 50
Other Drinking Water 100 39 90 150

Overall, 4053 samples were collected and analyized.

Survey Type and Number of Surveys per Deployment

Survey Type Overview:
Form Accra Atlanta Dakar Dhaka Kampala Kumasi Lusaka Maputo Siem Reap Vellore
Behavioral Survey
Household 1021 23 500 823 548 400 400 261 410 200
Community 30 20 28 10 16 16 8
School 20 20 35 9 16 16 8
Environmental Testing
Sample 837 47 300 1000 382 282 420 376 303 106
Lab 837 47 300 1000 382 282 420 376 303 106

In total, there were 4586 Household Surveys, 128 Community Surveys and 124 School Surveys conducted. Additionally, 4053 environmental samples were collected and subsequently analyzed in the laboratory.

Survey Type and Number of People per Deployment

Form Participants:
Form Accra Atlanta Dakar Dhaka Kampala Kumasi Lusaka Maputo Siem Reap Vellore
Household 1021 23 500 823 548 400 400 261 410 200
Community 420 300 501 112 240 298 117
School 435 300 597 114 320 313 151

Note: Household survey numbers include the number of surveys, whereas community and school surveys account for number of participants.

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Literature

Population Density

  • Cambodia, Siem Reap:
  • Bangladesh, Dhaka:
    Census 2011, Report: Female/ Male Population (Table C02), Population Density (Table C01)
  • Ghana, Accra:
    Census 2010, Report:
    Greater Accra Region: 1235.8 population density in 2010, 895.5 population density in 2000 (people per kmsq) (Table 4.4).
    90% of the population is considered urban population (Table 4.5).
  • Zambia, Lusaka:
    Census 2010, Report: Population density for Lusaka 63.5 (2000) and 100.1 (2010) people per kmsq (Table 2.7).
    Census 2010, Population.
  • Mozambique, Maputo:
    Note: added population information to meta_neighborhood.csv file. Sources: Ghana Statistical Services - Census 2010 Shapefile from Habib.

Enteric Disease Burden

Fecal Sludge Managment

Shit Flow Diagram

  • Cambodia, Siem Reap:
    no SFD available
  • Bangladesh, Dhaka:
    SFD Link. The SFD report from March 2016 indicates that less than 1% of fecal sludge in Dhaka is safely managed.
  • Ghana, Accra:
    no SFD available
  • Zambia, Lusaka:
    SFD Link. The SFD report from September 2018 shows that 17% of fecal sludge is safely managed.
  • Mozambique, Maputo:
    no SFD available
  • Ghana, Kumasi:
    SFD Link. The SFD report from November 2015 shows that 55% of fecal sludge is safely managed.

WASH Interventions in the past 10 Years

Local Stakeholders/ Decision Makers

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MF vs. IDEXX

How many samples were collected for each deployment and each pathway by laboratory analysis method: Membrane Filtration (MF) or IDEXX Quanti-Tray (IDEXX).

Lab Method used:
IDEXX MF
Accra 0 837
Atlanta 47 0
Dakar 0 300
Dhaka 1000 0
Kampala 0 382
Kumasi 0 282
Lusaka 420 0
Maputo 0 376
Siem Reap 0 303
Vellore 0 106
Lab Method per Sample Type:
IDEXX MF
Open Drain Water 150 343
Raw Produce 160 305
Municipal Drinking Water 150 335
Ocean 0 40
Surface Water 110 15
Floodwater 137 174
Public Latrine 160 452
Soil 160 468
Bathing Water 100 110
Street Food 150 155
Other Drinking Water 190 189

Membrane Filtration: valid vs. invalid test results

How many samples tested with the Membrane Filtration method produced valid or invalid results?
Note: Invalid MF results include TDTC (Too Dirty To Count) classifications of laboratory results of samples. Valid MF results are all results between the range of 0 and 200 as well as TNTC (Too Numerous To Count).

Overview: Valid Membrane Filtration Lab Results
n valid invalid percent valid
6030 5846 184 96.95
Valid Membrane Filtration Lab Results by Deployment
city n valid invalid percent valid
Accra 1905 1874 31 98.37
Dakar 602 601 1 99.83
Kampala 947 924 23 97.57
Kumasi 708 670 38 94.63
Maputo 917 840 77 91.6
Siem Reap 739 725 14 98.11
Vellore 212 212 0 100
Valid Membrane Filtration Lab Results by Sample Type
sample type n valid invalid percent valid
Open Drain Water 877 872 5 99.43
Raw Produce 770 742 28 96.36
Municipal Drinking Water 671 671 0 100
Ocean 80 80 0 100
Surface Water 32 31 1 96.88
Floodwater 495 485 10 97.98
Public Latrine 910 890 20 97.8
Soil 1185 1073 112 90.55
Bathing Water 220 219 1 99.55
Street Food 410 408 2 99.51
Other Drinking Water 380 375 5 98.68
Valid Membrane Filtration Lab Results by city and Sample Type
city sample type n valid invalid percent valid
Accra Open Drain Water 531 531 0 100
Accra Raw Produce 199 194 5 97.49
Accra Municipal Drinking Water 176 176 0 100
Accra Ocean 80 80 0 100
Accra Floodwater 45 45 0 100
Accra Public Latrine 560 545 15 97.32
Accra Soil 236 225 11 95.34
Accra Bathing Water 18 18 0 100
Accra Street Food 60 60 0 100
Dakar Open Drain Water 100 99 1 99
Dakar Raw Produce 100 100 0 100
Dakar Municipal Drinking Water 201 201 0 100
Dakar Soil 100 100 0 100
Dakar Street Food 101 101 0 100
Kampala Open Drain Water 100 97 3 97
Kampala Raw Produce 146 142 4 97.26
Kampala Municipal Drinking Water 78 78 0 100
Kampala Surface Water 26 25 1 96.15
Kampala Floodwater 143 143 0 100
Kampala Public Latrine 106 102 4 96.23
Kampala Soil 139 129 10 92.81
Kampala Street Food 129 128 1 99.22
Kampala Other Drinking Water 80 80 0 100
Kumasi Open Drain Water 80 79 1 98.75
Kumasi Raw Produce 117 105 12 89.74
Kumasi Municipal Drinking Water 72 72 0 100
Kumasi Surface Water 6 6 0 100
Kumasi Floodwater 97 94 3 96.91
Kumasi Public Latrine 54 53 1 98.15
Kumasi Soil 120 101 19 84.17
Kumasi Bathing Water 42 41 1 97.62
Kumasi Street Food 120 119 1 99.17
Maputo Open Drain Water 66 66 0 100
Maputo Raw Produce 69 67 2 97.1
Maputo Municipal Drinking Water 80 80 0 100
Maputo Floodwater 60 54 6 90
Maputo Public Latrine 142 142 0 100
Maputo Soil 400 331 69 82.75
Maputo Bathing Water 100 100 0 100
Siem Reap Raw Produce 99 94 5 94.95
Siem Reap Municipal Drinking Water 20 20 0 100
Siem Reap Floodwater 150 149 1 99.33
Siem Reap Soil 150 147 3 98
Siem Reap Bathing Water 20 20 0 100
Siem Reap Other Drinking Water 300 295 5 98.33
Vellore Raw Produce 40 40 0 100
Vellore Municipal Drinking Water 44 44 0 100
Vellore Public Latrine 48 48 0 100
Vellore Soil 40 40 0 100
Vellore Bathing Water 40 40 0 100

These results suggest sample types with a low percentage of validity of MF testing could improve the overall data quality by addressing potential sampling problems in training. Numbers in the “Percent Valid” column in RED indicate a rate below 80%.

IDEXX: valid vs. invalid test results

How many samples tested with IDEXX method produced a valid or invalid result?
Valid IDEXX Lab Results
n valid invalid percent valid
3668 3667 1 99.97

Dilution Error

When a dilution error occurs, meaning that a sample was not properly processed in the laboratory, the SaniPath Tool automatically discards the E. coli results of this sample (e.g. when the E. coli count for the second dilution is higher than the first dilution - including a variety of possible combinations of illogical results and potential errors).
This section explores how many samples were lost due to dilution error.

Dilution Error Overview:

Dilution Error by City
city omitted n percentage
Accra 18 837 2.15
Atlanta 0 47 0.00
Dakar 15 300 5.00
Dhaka 53 1000 5.30
Kampala 46 382 12.04
Kumasi 3 282 1.06
Lusaka 20 420 4.76
Maputo 22 376 5.85
Siem Reap 5 303 1.65
Vellore 9 106 8.49

Dilution Error By city, by neighborhood

Dilution Error by City and Neighborhood
city neighb_UID omitted n percentage
Accra 301 3 93 3.23
Accra 302 4 253 1.58
Accra 303 7 124 5.65
Accra 304 2 85 2.35
Accra 305 2 133 1.50
Dakar 1201 3 60 5.00
Dakar 1202 1 60 1.67
Dakar 1203 2 60 3.33
Dakar 1204 8 60 13.33
Dakar 1205 1 60 1.67
Dhaka 201 8 100 8.00
Dhaka 202 2 100 2.00
Dhaka 203 3 100 3.00
Dhaka 204 9 100 9.00
Dhaka 205 2 100 2.00
Dhaka 206 6 100 6.00
Dhaka 207 3 100 3.00
Dhaka 208 6 100 6.00
Dhaka 209 10 100 10.00
Dhaka 210 4 100 4.00
Kampala 901 14 67 20.90
Kampala 902 9 78 11.54
Kampala 903 2 78 2.56
Kampala 904 15 77 19.48
Kampala 905 6 82 7.32
Kumasi 701 2 68 2.94
Kumasi 702 1 79 1.27
Lusaka 401 18 170 10.59
Lusaka 1301 2 90 2.22
Maputo 501 7 98 7.14
Maputo 502 5 54 9.26
Maputo 801 8 152 5.26
Maputo 802 2 72 2.78
Siem Reap 101 2 70 2.86
Siem Reap 102 2 60 3.33
Siem Reap 103 1 49 2.04
Vellore 1101 2 53 3.77
Vellore 1102 7 53 13.21

Dilution Error By city, by neighborhood, by sample

Dilution Error by City and Neighborhood and Sample Type
city neighb_UID sample_type_name omitted n percentage
Accra 301 Open Drain Water 1 16 6.25
Accra 301 Raw Produce 1 20 5.00
Accra 301 Soil 1 18 5.56
Accra 302 Raw Produce 2 20 10.00
Accra 302 Public Latrine 1 95 1.05
Accra 302 Soil 1 40 2.50
Accra 303 Raw Produce 5 9 55.56
Accra 303 Public Latrine 2 59 3.39
Accra 304 Raw Produce 1 10 10.00
Accra 304 Public Latrine 1 29 3.45
Accra 305 Raw Produce 2 11 18.18
Dakar 1201 Soil 2 10 20.00
Dakar 1201 Street Food 1 10 10.00
Dakar 1202 Open Drain Water 1 10 10.00
Dakar 1203 Open Drain Water 1 10 10.00
Dakar 1203 Soil 1 10 10.00
Dakar 1204 Open Drain Water 7 10 70.00
Dakar 1204 Soil 1 10 10.00
Dakar 1205 Soil 1 10 10.00
Dhaka 201 Open Drain Water 1 10 10.00
Dhaka 201 Raw Produce 1 10 10.00
Dhaka 201 Floodwater 2 10 20.00
Dhaka 201 Soil 2 10 20.00
Dhaka 201 Street Food 2 10 20.00
Dhaka 202 Raw Produce 2 10 20.00
Dhaka 203 Raw Produce 1 10 10.00
Dhaka 203 Soil 1 10 10.00
Dhaka 203 Street Food 1 10 10.00
Dhaka 204 Raw Produce 2 10 20.00
Dhaka 204 Surface Water 1 10 10.00
Dhaka 204 Floodwater 2 10 20.00
Dhaka 204 Soil 1 10 10.00
Dhaka 204 Street Food 3 10 30.00
Dhaka 205 Floodwater 1 10 10.00
Dhaka 205 Street Food 1 10 10.00
Dhaka 206 Raw Produce 2 10 20.00
Dhaka 206 Surface Water 1 10 10.00
Dhaka 206 Floodwater 1 10 10.00
Dhaka 206 Soil 1 10 10.00
Dhaka 206 Street Food 1 10 10.00
Dhaka 207 Raw Produce 2 10 20.00
Dhaka 207 Soil 1 10 10.00
Dhaka 208 Raw Produce 1 10 10.00
Dhaka 208 Floodwater 1 10 10.00
Dhaka 208 Soil 2 10 20.00
Dhaka 208 Street Food 2 10 20.00
Dhaka 209 Raw Produce 2 10 20.00
Dhaka 209 Surface Water 2 10 20.00
Dhaka 209 Soil 2 10 20.00
Dhaka 209 Street Food 4 10 40.00
Dhaka 210 Surface Water 1 10 10.00
Dhaka 210 Soil 1 10 10.00
Dhaka 210 Street Food 2 10 20.00
Kampala 901 Raw Produce 1 10 10.00
Kampala 901 Surface Water 3 8 37.50
Kampala 901 Public Latrine 3 10 30.00
Kampala 901 Soil 2 10 20.00
Kampala 901 Street Food 5 10 50.00
Kampala 902 Raw Produce 4 10 40.00
Kampala 902 Soil 1 10 10.00
Kampala 902 Street Food 3 9 33.33
Kampala 902 Other Drinking Water 1 10 10.00
Kampala 903 Raw Produce 1 10 10.00
Kampala 903 Soil 1 10 10.00
Kampala 904 Raw Produce 3 10 30.00
Kampala 904 Floodwater 3 10 30.00
Kampala 904 Soil 4 10 40.00
Kampala 904 Street Food 4 10 40.00
Kampala 904 Other Drinking Water 1 10 10.00
Kampala 905 Floodwater 1 10 10.00
Kampala 905 Soil 2 10 20.00
Kampala 905 Other Drinking Water 3 10 30.00
Kumasi 701 Raw Produce 1 9 11.11
Kumasi 701 Street Food 1 10 10.00
Kumasi 702 Public Latrine 1 9 11.11
Lusaka 401 Open Drain Water 2 20 10.00
Lusaka 401 Raw Produce 1 20 5.00
Lusaka 401 Floodwater 10 30 33.33
Lusaka 401 Soil 4 20 20.00
Lusaka 401 Street Food 1 20 5.00
Lusaka 1301 Soil 2 10 20.00
Maputo 501 Open Drain Water 1 9 11.11
Maputo 501 Soil 5 54 9.26
Maputo 501 Bathing Water 1 15 6.67
Maputo 502 Open Drain Water 1 6 16.67
Maputo 502 Floodwater 2 5 40.00
Maputo 502 Soil 2 20 10.00
Maputo 801 Municipal Drinking Water 1 30 3.33
Maputo 801 Public Latrine 5 30 16.67
Maputo 801 Soil 1 57 1.75
Maputo 801 Bathing Water 1 15 6.67
Maputo 802 Raw Produce 1 8 12.50
Maputo 802 Municipal Drinking Water 1 10 10.00
Siem Reap 101 Raw Produce 2 10 20.00
Siem Reap 102 Raw Produce 2 10 20.00
Siem Reap 103 Other Drinking Water 1 29 3.45
Vellore 1101 Municipal Drinking Water 2 11 18.18
Vellore 1102 Municipal Drinking Water 2 11 18.18
Vellore 1102 Public Latrine 1 12 8.33
Vellore 1102 Soil 1 10 10.00
Vellore 1102 Bathing Water 3 10 30.00

Sample Size Minimum for Model:

To correctly perform the QMRA calculations, a minimum sample size of 10 samples has to be met. Which sample types per neighborhood and deployment have fulfilled the requirement? (omitted = sample discarded because of error)

Number of pathways that did not meet the requred 10 sample minimum, per deployment:
Requirements Not Met - Overview
citylabel neighb_UID not_met
Maputo 15 501 2
Maputo 15 502 2
Accra 16 303 2
Accra 16 304 1
Accra 16 305 1
Maputo 16 801 1
Maputo 16 802 3
Siem Reap 16 105 1
Atlanta 16 1001 1
Accra 18 601 2
Accra 18 602 2
Kumasi 18 701 3
Kumasi 18 702 1
Kumasi 18 703 3
Kumasi 18 704 2
Kampala 18 901 2
Kampala 18 902 2
Kampala 18 903 4
Kampala 18 904 1
Minimum Requirement Met?
citylabel neighb_UID sample_type_name omitted n requ_met net_n net_requ_met
Vellore 14 1101 Raw Produce 0 10 Yes 10 Yes
Vellore 14 1101 Municipal Drinking Water 2 11 Yes 9 No
Vellore 14 1101 Public Latrine 0 12 Yes 12 Yes
Vellore 14 1101 Soil 0 10 Yes 10 Yes
Vellore 14 1101 Bathing Water 0 10 Yes 10 Yes
Vellore 14 1102 Raw Produce 0 10 Yes 10 Yes
Vellore 14 1102 Municipal Drinking Water 2 11 Yes 9 No
Vellore 14 1102 Public Latrine 1 12 Yes 11 Yes
Vellore 14 1102 Soil 1 10 Yes 9 No
Vellore 14 1102 Bathing Water 3 10 Yes 7 No
Maputo 15 501 Open Drain Water 1 9 No 8 No
Maputo 15 501 Floodwater 0 5 No 5 No
Maputo 15 501 Public Latrine 0 15 Yes 15 Yes
Maputo 15 501 Soil 5 54 Yes 49 Yes
Maputo 15 501 Bathing Water 1 15 Yes 14 Yes
Maputo 15 502 Open Drain Water 1 6 No 5 No
Maputo 15 502 Floodwater 2 5 No 3 No
Maputo 15 502 Public Latrine 0 13 Yes 13 Yes
Maputo 15 502 Soil 2 20 Yes 18 Yes
Maputo 15 502 Bathing Water 0 10 Yes 10 Yes
Accra 16 301 Open Drain Water 1 16 Yes 15 Yes
Accra 16 301 Raw Produce 1 20 Yes 19 Yes
Accra 16 301 Municipal Drinking Water 0 10 Yes 10 Yes
Accra 16 301 Ocean 0 10 Yes 10 Yes
Accra 16 301 Public Latrine 0 19 Yes 19 Yes
Accra 16 301 Soil 1 18 Yes 17 Yes
Accra 16 302 Open Drain Water 0 48 Yes 48 Yes
Accra 16 302 Raw Produce 2 20 Yes 18 Yes
Accra 16 302 Municipal Drinking Water 0 30 Yes 30 Yes
Accra 16 302 Ocean 0 20 Yes 20 Yes
Accra 16 302 Public Latrine 1 95 Yes 94 Yes
Accra 16 302 Soil 1 40 Yes 39 Yes
Accra 16 303 Open Drain Water 0 37 Yes 37 Yes
Accra 16 303 Raw Produce 5 9 No 4 No
Accra 16 303 Municipal Drinking Water 0 9 No 9 No
Accra 16 303 Public Latrine 2 59 Yes 57 Yes
Accra 16 303 Soil 0 10 Yes 10 Yes
Accra 16 304 Open Drain Water 0 27 Yes 27 Yes
Accra 16 304 Raw Produce 1 10 Yes 9 No
Accra 16 304 Municipal Drinking Water 0 9 No 9 No
Accra 16 304 Public Latrine 1 29 Yes 28 Yes
Accra 16 304 Soil 0 10 Yes 10 Yes
Accra 16 305 Open Drain Water 0 35 Yes 35 Yes
Accra 16 305 Raw Produce 2 11 Yes 9 No
Accra 16 305 Municipal Drinking Water 0 10 Yes 10 Yes
Accra 16 305 Floodwater 0 9 No 9 No
Accra 16 305 Public Latrine 0 58 Yes 58 Yes
Accra 16 305 Soil 0 10 Yes 10 Yes
Maputo 16 801 Raw Produce 0 15 Yes 15 Yes
Maputo 16 801 Municipal Drinking Water 1 30 Yes 29 Yes
Maputo 16 801 Floodwater 0 5 No 5 No
Maputo 16 801 Public Latrine 5 30 Yes 25 Yes
Maputo 16 801 Soil 1 57 Yes 56 Yes
Maputo 16 801 Bathing Water 1 15 Yes 14 Yes
Maputo 16 802 Open Drain Water 0 7 No 7 No
Maputo 16 802 Raw Produce 1 8 No 7 No
Maputo 16 802 Municipal Drinking Water 1 10 Yes 9 No
Maputo 16 802 Floodwater 0 5 No 5 No
Maputo 16 802 Public Latrine 0 13 Yes 13 Yes
Maputo 16 802 Soil 0 19 Yes 19 Yes
Maputo 16 802 Bathing Water 0 10 Yes 10 Yes
Siem Reap 16 101 Raw Produce 2 10 Yes 8 No
Siem Reap 16 101 Floodwater 0 10 Yes 10 Yes
Siem Reap 16 101 Soil 0 10 Yes 10 Yes
Siem Reap 16 101 Bathing Water 0 10 Yes 10 Yes
Siem Reap 16 101 Other Drinking Water 0 30 Yes 30 Yes
Siem Reap 16 102 Raw Produce 2 10 Yes 8 No
Siem Reap 16 102 Floodwater 0 10 Yes 10 Yes
Siem Reap 16 102 Soil 0 10 Yes 10 Yes
Siem Reap 16 102 Other Drinking Water 0 30 Yes 30 Yes
Siem Reap 16 103 Floodwater 0 10 Yes 10 Yes
Siem Reap 16 103 Soil 0 10 Yes 10 Yes
Siem Reap 16 103 Other Drinking Water 1 29 Yes 28 Yes
Siem Reap 16 104 Raw Produce 0 10 Yes 10 Yes
Siem Reap 16 104 Municipal Drinking Water 0 10 Yes 10 Yes
Siem Reap 16 104 Floodwater 0 10 Yes 10 Yes
Siem Reap 16 104 Soil 0 10 Yes 10 Yes
Siem Reap 16 104 Other Drinking Water 0 31 Yes 31 Yes
Siem Reap 16 105 Raw Produce 0 3 No 3 No
Siem Reap 16 105 Floodwater 0 10 Yes 10 Yes
Siem Reap 16 105 Soil 0 10 Yes 10 Yes
Siem Reap 16 105 Other Drinking Water 0 30 Yes 30 Yes
Atlanta 16 1001 Raw Produce 0 10 Yes 10 Yes
Atlanta 16 1001 Municipal Drinking Water 0 10 Yes 10 Yes
Atlanta 16 1001 Floodwater 0 7 No 7 No
Atlanta 16 1001 Public Latrine 0 10 Yes 10 Yes
Atlanta 16 1001 Soil 0 10 Yes 10 Yes
Dhaka 17 201 Open Drain Water 1 10 Yes 9 No
Dhaka 17 201 Raw Produce 1 10 Yes 9 No
Dhaka 17 201 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 201 Surface Water 0 10 Yes 10 Yes
Dhaka 17 201 Floodwater 2 10 Yes 8 No
Dhaka 17 201 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 201 Soil 2 10 Yes 8 No
Dhaka 17 201 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 201 Street Food 2 10 Yes 8 No
Dhaka 17 201 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 202 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 202 Raw Produce 2 10 Yes 8 No
Dhaka 17 202 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 202 Surface Water 0 10 Yes 10 Yes
Dhaka 17 202 Floodwater 0 10 Yes 10 Yes
Dhaka 17 202 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 202 Soil 0 10 Yes 10 Yes
Dhaka 17 202 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 202 Street Food 0 10 Yes 10 Yes
Dhaka 17 202 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 203 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 203 Raw Produce 1 10 Yes 9 No
Dhaka 17 203 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 203 Surface Water 0 10 Yes 10 Yes
Dhaka 17 203 Floodwater 0 10 Yes 10 Yes
Dhaka 17 203 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 203 Soil 1 10 Yes 9 No
Dhaka 17 203 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 203 Street Food 1 10 Yes 9 No
Dhaka 17 203 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 204 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 204 Raw Produce 2 10 Yes 8 No
Dhaka 17 204 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 204 Surface Water 1 10 Yes 9 No
Dhaka 17 204 Floodwater 2 10 Yes 8 No
Dhaka 17 204 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 204 Soil 1 10 Yes 9 No
Dhaka 17 204 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 204 Street Food 3 10 Yes 7 No
Dhaka 17 204 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 205 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 205 Raw Produce 0 10 Yes 10 Yes
Dhaka 17 205 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 205 Surface Water 0 10 Yes 10 Yes
Dhaka 17 205 Floodwater 1 10 Yes 9 No
Dhaka 17 205 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 205 Soil 0 10 Yes 10 Yes
Dhaka 17 205 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 205 Street Food 1 10 Yes 9 No
Dhaka 17 205 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 206 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 206 Raw Produce 2 10 Yes 8 No
Dhaka 17 206 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 206 Surface Water 1 10 Yes 9 No
Dhaka 17 206 Floodwater 1 10 Yes 9 No
Dhaka 17 206 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 206 Soil 1 10 Yes 9 No
Dhaka 17 206 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 206 Street Food 1 10 Yes 9 No
Dhaka 17 206 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 207 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 207 Raw Produce 2 10 Yes 8 No
Dhaka 17 207 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 207 Surface Water 0 10 Yes 10 Yes
Dhaka 17 207 Floodwater 0 10 Yes 10 Yes
Dhaka 17 207 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 207 Soil 1 10 Yes 9 No
Dhaka 17 207 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 207 Street Food 0 10 Yes 10 Yes
Dhaka 17 207 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 208 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 208 Raw Produce 1 10 Yes 9 No
Dhaka 17 208 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 208 Surface Water 0 10 Yes 10 Yes
Dhaka 17 208 Floodwater 1 10 Yes 9 No
Dhaka 17 208 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 208 Soil 2 10 Yes 8 No
Dhaka 17 208 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 208 Street Food 2 10 Yes 8 No
Dhaka 17 208 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 209 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 209 Raw Produce 2 10 Yes 8 No
Dhaka 17 209 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 209 Surface Water 2 10 Yes 8 No
Dhaka 17 209 Floodwater 0 10 Yes 10 Yes
Dhaka 17 209 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 209 Soil 2 10 Yes 8 No
Dhaka 17 209 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 209 Street Food 4 10 Yes 6 No
Dhaka 17 209 Other Drinking Water 0 10 Yes 10 Yes
Dhaka 17 210 Open Drain Water 0 10 Yes 10 Yes
Dhaka 17 210 Raw Produce 0 10 Yes 10 Yes
Dhaka 17 210 Municipal Drinking Water 0 10 Yes 10 Yes
Dhaka 17 210 Surface Water 1 10 Yes 9 No
Dhaka 17 210 Floodwater 0 10 Yes 10 Yes
Dhaka 17 210 Public Latrine 0 10 Yes 10 Yes
Dhaka 17 210 Soil 1 10 Yes 9 No
Dhaka 17 210 Bathing Water 0 10 Yes 10 Yes
Dhaka 17 210 Street Food 2 10 Yes 8 No
Dhaka 17 210 Other Drinking Water 0 10 Yes 10 Yes
Lusaka 18 401 Open Drain Water 2 20 Yes 18 Yes
Lusaka 18 401 Raw Produce 1 20 Yes 19 Yes
Lusaka 18 401 Municipal Drinking Water 0 10 Yes 10 Yes
Lusaka 18 401 Floodwater 10 30 Yes 20 Yes
Lusaka 18 401 Public Latrine 0 20 Yes 20 Yes
Lusaka 18 401 Soil 4 20 Yes 16 Yes
Lusaka 18 401 Street Food 1 20 Yes 19 Yes
Lusaka 18 401 Other Drinking Water 0 30 Yes 30 Yes
Accra 18 601 Open Drain Water 0 10 Yes 10 Yes
Accra 18 601 Raw Produce 0 10 Yes 10 Yes
Accra 18 601 Municipal Drinking Water 0 10 Yes 10 Yes
Accra 18 601 Floodwater 0 4 No 4 No
Accra 18 601 Public Latrine 0 10 Yes 10 Yes
Accra 18 601 Soil 0 10 Yes 10 Yes
Accra 18 601 Bathing Water 0 4 No 4 No
Accra 18 601 Street Food 0 10 Yes 10 Yes
Accra 18 602 Open Drain Water 0 11 Yes 11 Yes
Accra 18 602 Raw Produce 0 10 Yes 10 Yes
Accra 18 602 Municipal Drinking Water 0 10 Yes 10 Yes
Accra 18 602 Ocean 0 10 Yes 10 Yes
Accra 18 602 Floodwater 0 5 No 5 No
Accra 18 602 Public Latrine 0 10 Yes 10 Yes
Accra 18 602 Soil 0 10 Yes 10 Yes
Accra 18 602 Bathing Water 0 5 No 5 No
Accra 18 602 Street Food 0 10 Yes 10 Yes
Kumasi 18 701 Open Drain Water 0 10 Yes 10 Yes
Kumasi 18 701 Raw Produce 1 9 No 8 No
Kumasi 18 701 Municipal Drinking Water 0 10 Yes 10 Yes
Kumasi 18 701 Floodwater 0 6 No 6 No
Kumasi 18 701 Public Latrine 0 10 Yes 10 Yes
Kumasi 18 701 Soil 0 10 Yes 10 Yes
Kumasi 18 701 Bathing Water 0 3 No 3 No
Kumasi 18 701 Street Food 1 10 Yes 9 No
Kumasi 18 702 Open Drain Water 0 10 Yes 10 Yes
Kumasi 18 702 Raw Produce 0 10 Yes 10 Yes
Kumasi 18 702 Municipal Drinking Water 0 10 Yes 10 Yes
Kumasi 18 702 Floodwater 0 10 Yes 10 Yes
Kumasi 18 702 Public Latrine 1 9 No 8 No
Kumasi 18 702 Soil 0 10 Yes 10 Yes
Kumasi 18 702 Bathing Water 0 10 Yes 10 Yes
Kumasi 18 702 Street Food 0 10 Yes 10 Yes
Kumasi 18 703 Open Drain Water 0 10 Yes 10 Yes
Kumasi 18 703 Raw Produce 0 10 Yes 10 Yes
Kumasi 18 703 Municipal Drinking Water 0 10 Yes 10 Yes
Kumasi 18 703 Surface Water 0 3 No 3 No
Kumasi 18 703 Floodwater 0 10 Yes 10 Yes
Kumasi 18 703 Public Latrine 0 4 No 4 No
Kumasi 18 703 Soil 0 10 Yes 10 Yes
Kumasi 18 703 Bathing Water 0 8 No 8 No
Kumasi 18 703 Street Food 0 10 Yes 10 Yes
Kumasi 18 704 Open Drain Water 0 10 Yes 10 Yes
Kumasi 18 704 Raw Produce 0 10 Yes 10 Yes
Kumasi 18 704 Municipal Drinking Water 0 6 No 6 No
Kumasi 18 704 Floodwater 0 10 Yes 10 Yes
Kumasi 18 704 Public Latrine 0 4 No 4 No
Kumasi 18 704 Soil 0 10 Yes 10 Yes
Kumasi 18 704 Street Food 0 10 Yes 10 Yes
Kampala 18 901 Open Drain Water 0 9 No 9 No
Kampala 18 901 Raw Produce 1 10 Yes 9 No
Kampala 18 901 Surface Water 3 8 No 5 No
Kampala 18 901 Floodwater 0 10 Yes 10 Yes
Kampala 18 901 Public Latrine 3 10 Yes 7 No
Kampala 18 901 Soil 2 10 Yes 8 No
Kampala 18 901 Street Food 5 10 Yes 5 No
Kampala 18 902 Open Drain Water 0 9 No 9 No
Kampala 18 902 Raw Produce 4 10 Yes 6 No
Kampala 18 902 Municipal Drinking Water 0 10 Yes 10 Yes
Kampala 18 902 Floodwater 0 10 Yes 10 Yes
Kampala 18 902 Public Latrine 0 10 Yes 10 Yes
Kampala 18 902 Soil 1 10 Yes 9 No
Kampala 18 902 Street Food 3 9 No 6 No
Kampala 18 902 Other Drinking Water 1 10 Yes 9 No
Kampala 18 903 Open Drain Water 0 10 Yes 10 Yes
Kampala 18 903 Raw Produce 1 10 Yes 9 No
Kampala 18 903 Municipal Drinking Water 0 9 No 9 No
Kampala 18 903 Surface Water 0 4 No 4 No
Kampala 18 903 Floodwater 0 10 Yes 10 Yes
Kampala 18 903 Public Latrine 0 10 Yes 10 Yes
Kampala 18 903 Soil 1 10 Yes 9 No
Kampala 18 903 Street Food 0 6 No 6 No
Kampala 18 903 Other Drinking Water 0 9 No 9 No
Kampala 18 904 Open Drain Water 0 7 No 7 No
Kampala 18 904 Raw Produce 3 10 Yes 7 No
Kampala 18 904 Municipal Drinking Water 0 10 Yes 10 Yes
Kampala 18 904 Floodwater 3 10 Yes 7 No
Kampala 18 904 Public Latrine 0 10 Yes 10 Yes
Kampala 18 904 Soil 4 10 Yes 6 No
Kampala 18 904 Street Food 4 10 Yes 6 No
Kampala 18 904 Other Drinking Water 1 10 Yes 9 No
Kampala 18 905 Open Drain Water 0 12 Yes 12 Yes
Kampala 18 905 Raw Produce 0 10 Yes 10 Yes
Kampala 18 905 Municipal Drinking Water 0 10 Yes 10 Yes
Kampala 18 905 Floodwater 1 10 Yes 9 No
Kampala 18 905 Public Latrine 0 10 Yes 10 Yes
Kampala 18 905 Soil 2 10 Yes 8 No
Kampala 18 905 Street Food 0 10 Yes 10 Yes
Kampala 18 905 Other Drinking Water 3 10 Yes 7 No
Lusaka 19 1301 Open Drain Water 0 10 Yes 10 Yes
Lusaka 19 1301 Raw Produce 0 10 Yes 10 Yes
Lusaka 19 1301 Municipal Drinking Water 0 10 Yes 10 Yes
Lusaka 19 1301 Surface Water 0 10 Yes 10 Yes
Lusaka 19 1301 Public Latrine 0 10 Yes 10 Yes
Lusaka 19 1301 Soil 2 10 Yes 8 No
Lusaka 19 1301 Street Food 0 10 Yes 10 Yes
Lusaka 19 1301 Other Drinking Water 0 20 Yes 20 Yes
Lusaka 19 1302 Open Drain Water 0 10 Yes 10 Yes
Lusaka 19 1302 Raw Produce 0 10 Yes 10 Yes
Lusaka 19 1302 Municipal Drinking Water 0 10 Yes 10 Yes
Lusaka 19 1302 Public Latrine 0 10 Yes 10 Yes
Lusaka 19 1302 Soil 0 10 Yes 10 Yes
Lusaka 19 1302 Street Food 0 10 Yes 10 Yes
Lusaka 19 1302 Other Drinking Water 0 20 Yes 20 Yes
Lusaka 19 1303 Open Drain Water 0 10 Yes 10 Yes
Lusaka 19 1303 Raw Produce 0 10 Yes 10 Yes
Lusaka 19 1303 Municipal Drinking Water 0 10 Yes 10 Yes
Lusaka 19 1303 Public Latrine 0 10 Yes 10 Yes
Lusaka 19 1303 Soil 0 10 Yes 10 Yes
Lusaka 19 1303 Street Food 0 10 Yes 10 Yes
Lusaka 19 1303 Other Drinking Water 0 20 Yes 20 Yes
Dakar 19 1201 Open Drain Water 0 10 Yes 10 Yes
Dakar 19 1201 Raw Produce 0 10 Yes 10 Yes
Dakar 19 1201 Municipal Drinking Water 0 20 Yes 20 Yes
Dakar 19 1201 Soil 2 10 Yes 8 No
Dakar 19 1201 Street Food 1 10 Yes 9 No
Dakar 19 1202 Open Drain Water 1 10 Yes 9 No
Dakar 19 1202 Raw Produce 0 10 Yes 10 Yes
Dakar 19 1202 Municipal Drinking Water 0 20 Yes 20 Yes
Dakar 19 1202 Soil 0 10 Yes 10 Yes
Dakar 19 1202 Street Food 0 10 Yes 10 Yes
Dakar 19 1203 Open Drain Water 1 10 Yes 9 No
Dakar 19 1203 Raw Produce 0 10 Yes 10 Yes
Dakar 19 1203 Municipal Drinking Water 0 20 Yes 20 Yes
Dakar 19 1203 Soil 1 10 Yes 9 No
Dakar 19 1203 Street Food 0 10 Yes 10 Yes
Dakar 19 1204 Open Drain Water 7 10 Yes 3 No
Dakar 19 1204 Raw Produce 0 10 Yes 10 Yes
Dakar 19 1204 Municipal Drinking Water 0 20 Yes 20 Yes
Dakar 19 1204 Soil 1 10 Yes 9 No
Dakar 19 1204 Street Food 0 10 Yes 10 Yes
Dakar 19 1205 Open Drain Water 0 10 Yes 10 Yes
Dakar 19 1205 Raw Produce 0 10 Yes 10 Yes
Dakar 19 1205 Municipal Drinking Water 0 20 Yes 20 Yes
Dakar 19 1205 Soil 1 10 Yes 9 No
Dakar 19 1205 Street Food 0 10 Yes 10 Yes

Sample Types

E. coli count per Sample Types
sample_type_name n min ecoli max ecoli mean ecoli standard deviation variance
Open Drain Water 493 -0.30 9.58 7.69 8.39 16.77
Raw Produce 465 1.40 7.00 5.29 6.04 12.08
Municipal Drinking Water 485 -0.30 4.76 2.70 3.55 7.10
Ocean 40 1.95 6.30 5.59 5.79 11.59
Surface Water 125 1.30 7.38 5.99 6.49 12.97
Floodwater 311 0.70 8.08 6.20 6.88 13.77
Public Latrine 612 -0.15 4.45 3.03 3.66 7.31
Soil 628 -1.00 5.60 3.97 4.68 9.35
Bathing Water 210 -0.30 4.38 2.85 3.48 6.97
Street Food 305 0.69 6.55 4.82 5.52 11.04
Other Drinking Water 379 -0.30 3.86 2.37 2.90 5.80

Samples - Membrane Filtration

E. coli count per Sample Types - Membrane Filtration
sample_type_name n min ecoli max ecoli mean ecoli standard deviation variance
Open Drain Water 343 -0.30 9.58 7.77 8.46 16.92
Raw Produce 305 1.40 7.00 5.44 6.13 12.25
Municipal Drinking Water 335 -0.30 3.30 1.47 2.28 4.55
Ocean 40 1.95 6.30 5.59 5.79 11.59
Surface Water 15 1.48 4.94 4.00 4.39 8.79
Floodwater 174 0.70 7.27 5.91 6.33 12.67
Public Latrine 452 -0.15 4.45 3.16 3.72 7.44
Soil 468 -1.00 5.60 4.05 4.73 9.46
Bathing Water 110 -0.30 3.88 2.44 2.93 5.87
Street Food 155 1.32 6.55 4.73 5.56 11.11
Other Drinking Water 189 -0.30 3.40 2.16 2.62 5.24

Samples - IDEXX

E. coli count per Sample Types - IDEXX
sample_type_name n min ecoli max ecoli mean ecoli standard deviation variance
Open Drain Water 150 2.00 8.71 7.40 7.78 15.56
Raw Produce 160 1.40 6.06 4.64 5.22 10.44
Municipal Drinking Water 150 -0.30 4.76 3.18 3.79 7.59
Surface Water 110 1.30 7.38 6.04 6.51 13.02
Floodwater 137 1.70 8.08 6.43 7.06 14.12
Public Latrine 160 -0.15 2.80 1.13 1.77 3.55
Soil 160 0.00 5.09 3.57 4.21 8.43
Bathing Water 100 -0.30 4.38 3.06 3.63 7.26
Street Food 150 0.69 6.44 4.91 5.47 10.94
Other Drinking Water 190 -0.30 3.86 2.51 3.01 6.03
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Produce

Contamination of Produce

Differences within Produce Samples

General comparison tables indicating how many samples were taken overall.

Types of Produce and Number of Samples
id produce n
1 cabbage 33
2 cucumber 73
3 egg plant 2
4 lettuce 34
5 long bean 2
6 long plant 2
7 salad 14
8 tomato 162
9 water mimosa 1
10 wing bean 2
11 apple 6
12 carrot 11
13 pepper 66
14 spring onion 3
15 guava 4
16 coriander 35
17 green chilly 10
18 okra 3
19 watermelon 1
Types of Produce, by city
city n produce
Accra 4 cabbage
Accra 1 cucumber
Accra 6 lettuce
Accra 31 tomato
Accra 1 apple
Accra 3 carrot
Accra 41 pepper
Accra 3 spring onion
Atlanta 1 cucumber
Atlanta 2 lettuce
Atlanta 3 tomato
Atlanta 3 pepper
Dakar 9 cucumber
Dakar 10 salad
Dakar 9 tomato
Dakar 8 carrot
Dakar 9 pepper
Dakar 5 green chilly
Dhaka 35 cucumber
Dhaka 34 tomato
Dhaka 31 coriander
Kampala 25 cabbage
Kampala 25 tomato
Kumasi 13 lettuce
Kumasi 13 tomato
Kumasi 13 pepper
Lusaka 12 cucumber
Lusaka 28 tomato
Lusaka 5 apple
Lusaka 4 guava
Lusaka 1 watermelon
Maputo 7 cucumber
Maputo 8 lettuce
Maputo 8 tomato
Siem Reap 4 cabbage
Siem Reap 8 cucumber
Siem Reap 2 egg plant
Siem Reap 5 lettuce
Siem Reap 2 long bean
Siem Reap 2 long plant
Siem Reap 4 salad
Siem Reap 3 tomato
Siem Reap 1 water mimosa
Siem Reap 2 wing bean
Vellore 8 tomato
Vellore 4 coriander
Vellore 5 green chilly
Vellore 3 okra
E. coli count per Types of Produce
produce n min ecoli max ecoli mean ecoli standard deviation variance
apple 6 1.40 2.41 1.81 1.98 3.96
cabbage 33 1.40 5.73 4.68 5.05 10.09
carrot 11 2.40 5.22 4.48 4.71 9.41
coriander 35 2.70 6.06 5.28 5.51 11.02
cucumber 73 1.40 7.00 5.69 6.32 12.64
egg plant 2 4.11 4.83 4.61 4.59 9.19
green chilly 10 2.40 6.00 5.34 5.61 11.21
guava 4 1.40 5.50 5.02 5.26 10.52
lettuce 34 1.40 7.00 5.65 6.24 12.48
long bean 2 2.00 2.48 2.30 2.15 4.30
long plant 2 3.22 3.48 3.37 2.98 5.96
okra 3 3.57 4.10 3.83 3.70 7.39
pepper 66 1.40 6.00 5.18 5.50 11.00
salad 14 2.40 5.82 5.20 5.29 10.58
spring onion 3 2.60 4.60 4.31 4.45 8.89
tomato 162 1.40 7.00 4.99 5.91 11.83
water mimosa 1 4.80 4.80 4.80
watermelon 1 1.40 1.40 1.40
wing bean 2 2.81 4.81 4.51 4.65 9.30
1 1.40 1.40 1.40

Graphs - Differences within Produce Samples

E. coli count by deployment and neighborhood and type of produce

E. coli count by deployment and neighborhood and type of produce

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Latrines

Latrines Types from Sample Collection

Types of Latrines in all deployments
n Type
45 Flush/pour flush to pit
310 Flush/pour flush to septic tank
66 Flush/pour flush to sewage system
70 Pit latrine with slab
75 VIP
4 Open pit latrine (without slab)
8 Other
## Warning: Column `col_UID`/`UID` joining factor and character vector, coercing
## into character vector
Types of Latrines by deployment
city n Type
Accra 259 Flush/pour flush to septic tank
Accra 20 VIP
Accra 1 Other
Dhaka 6 Flush/pour flush to septic tank
Dhaka 64 Flush/pour flush to sewage system
Dhaka 10 Pit latrine with slab
Dhaka 12 VIP
Dhaka 4 Open pit latrine (without slab)
Dhaka 4 Other
Kampala 4 Flush/pour flush to pit
Kampala 8 Flush/pour flush to septic tank
Kampala 1 Flush/pour flush to sewage system
Kampala 13 Pit latrine with slab
Kampala 23 VIP
Kampala 1 Other
Kumasi 20 Flush/pour flush to septic tank
Kumasi 7 Pit latrine with slab
Lusaka 18 Flush/pour flush to pit
Lusaka 17 Flush/pour flush to septic tank
Lusaka 1 Flush/pour flush to sewage system
Lusaka 11 Pit latrine with slab
Lusaka 3 VIP
Maputo 23 Flush/pour flush to pit
Maputo 29 Pit latrine with slab
Maputo 17 VIP
Maputo 2 Other
Overall E. coli count per Types of Latrines
Type n min ecoli max ecoli mean ecoli standard deviation variance
Flush/pour flush to pit 45 -0.15 4.45 2.89 3.65 7.29
Flush/pour flush to septic tank 310 -0.15 4.45 3.21 3.76 7.52
Flush/pour flush to sewage system 66 -0.15 3.05 1.58 2.20 4.41
Pit latrine with slab 70 -0.15 3.45 2.30 2.80 5.61
VIP 75 -0.15 4.45 2.98 3.58 7.15
Open pit latrine (without slab) 4 -0.15 1.85 1.26 1.54 3.09
Other 8 -0.15 2.75 1.86 2.29 4.59
34 -0.15 3.45 2.89 3.06 6.12
E. coli count per Types of Latrines by city
Type city n min ecoli max ecoli mean ecoli standard deviation variance
Flush/pour flush to septic tank Accra 259 -0.15 4.45 3.27 3.80 7.59
VIP Accra 20 -0.15 4.45 3.52 3.83 7.66
Other Accra 1 -0.15 -0.15 -0.15
Atlanta 10 -0.15 -0.15 -0.15 -Inf -Inf
Flush/pour flush to septic tank Dhaka 6 -0.15 1.88 1.13 1.49 2.98
Flush/pour flush to sewage system Dhaka 64 -0.15 2.80 1.33 1.94 3.88
Pit latrine with slab Dhaka 10 -0.15 2.10 1.45 1.69 3.37
VIP Dhaka 12 -0.15 1.19 0.30 0.63 1.25
Open pit latrine (without slab) Dhaka 4 -0.15 1.85 1.26 1.54 3.09
Other Dhaka 4 -0.15 -0.15 -0.15 -Inf -Inf
Flush/pour flush to pit Kampala 4 -0.15 2.75 2.30 2.49 4.99
Flush/pour flush to septic tank Kampala 8 -0.15 2.92 2.08 2.47 4.93
Flush/pour flush to sewage system Kampala 1 3.05 3.05 3.05
Pit latrine with slab Kampala 13 -0.15 3.23 2.43 2.78 5.57
VIP Kampala 23 -0.15 3.53 2.23 2.85 5.71
Other Kampala 1 1.29 1.29 1.29
Flush/pour flush to septic tank Kumasi 20 -0.15 4.05 2.90 3.43 6.85
Pit latrine with slab Kumasi 7 -0.15 3.10 2.27 2.68 5.35
Flush/pour flush to pit Lusaka 18 -0.15 1.30 0.33 0.65 1.31
Flush/pour flush to septic tank Lusaka 17 -0.15 2.28 1.21 1.66 3.33
Flush/pour flush to sewage system Lusaka 1 -0.15 -0.15 -0.15
Pit latrine with slab Lusaka 11 -0.15 0.15 -0.08 -0.55 -1.10
VIP Lusaka 3 -0.15 -0.15 -0.15 -Inf -Inf
Flush/pour flush to pit Maputo 23 -0.15 4.45 3.21 3.81 7.61
Pit latrine with slab Maputo 29 -0.15 3.45 2.48 2.94 5.87
VIP Maputo 17 -0.15 1.84 0.93 1.28 2.56
Other Maputo 2 0.45 2.75 2.45 2.60 5.19
Vellore 24 -0.15 3.45 3.04 3.09 6.18
E. coli count of all latrines by city
city Neighborhood n min ecoli max ecoli mean ecoli standard deviation variance
Accra 301 19 -0.15 4.45 3.72 3.94 7.88
Accra 302 95 -0.15 4.02 2.59 3.22 6.45
Accra 303 59 -0.15 4.45 3.56 3.94 7.88
Accra 304 29 -0.15 4.45 3.34 3.87 7.73
Accra 305 58 -0.15 4.45 3.37 3.84 7.69
Accra 601 10 -0.15 1.45 0.55 0.93 1.87
Accra 602 10 -0.15 2.62 1.69 2.12 4.23
Atlanta 1001 10 -0.15 -0.15 -0.15 -Inf -Inf
Dhaka 201 10 -0.15 1.88 0.92 1.38 2.76
Dhaka 202 10 -0.15 1.55 0.80 1.06 2.13
Dhaka 203 10 -0.15 1.85 0.89 1.34 2.69
Dhaka 204 10 -0.15 2.42 1.50 1.92 3.83
Dhaka 205 10 -0.15 -0.15 -0.15 -Inf -Inf
Dhaka 206 10 -0.15 2.80 1.89 2.29 4.58
Dhaka 207 10 -0.15 0.76 0.08 0.20 0.40
Dhaka 208 10 -0.15 2.10 1.48 1.68 3.36
Dhaka 209 10 -0.15 2.25 1.27 1.75 3.50
Dhaka 210 10 -0.15 0.76 0.11 0.20 0.40
Kampala 901 10 -0.15 1.83 1.22 1.43 2.86
Kampala 902 10 -0.15 3.23 2.28 2.73 5.46
Kampala 903 10 0.85 3.15 2.61 2.73 5.46
Kampala 904 10 -0.15 -0.15 -0.15 -Inf -Inf
Kampala 905 10 -0.15 3.53 2.55 3.02 6.05
Kumasi 701 10 0.75 3.10 2.16 2.59 5.19
Kumasi 702 9 -0.15 4.05 3.27 3.60 7.21
Kumasi 703 4 -0.15 1.32 0.97 0.95 1.90
Kumasi 704 4 -0.15 0.62 0.32 0.23 0.47
Lusaka 401 20 -0.15 0.15 -0.11 -0.67 -1.33
Lusaka 1301 10 -0.15 2.28 1.40 1.77 3.55
Lusaka 1302 10 -0.15 1.45 0.59 0.93 1.87
Lusaka 1303 10 -0.15 1.02 0.31 0.50 1.01
Maputo 501 15 -0.15 3.45 2.27 2.86 5.72
Maputo 502 13 -0.15 3.45 2.67 3.02 6.03
Maputo 801 30 -0.15 3.23 2.04 2.55 5.10
Maputo 802 13 -0.15 4.45 3.34 3.89 7.78
Vellore 1101 12 0.15 3.45 3.07 3.10 6.20
Vellore 1102 12 -0.15 3.45 3.02 3.10 6.19

Graphs - Differences within Latrine Types

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Streetfood

Differences within Streetfood

Streetfood is standardized as one serving size of food available from street vendors.

## Warning: Column `col_UID`/`UID` joining factor and character vector, coercing
## into character vector
Streetfood E. coli count per city
city n min ecoli max ecoli mean ecoli standard deviation variance
Accra 20 1.95 6.55 5.52 5.96 11.93
Dakar 50 2.67 3.83 2.88 3.01 6.01
Dhaka 100 0.69 6.44 5.11 5.56 11.12
Kampala 45 1.54 5.56 4.37 4.86 9.72
Kumasi 40 1.32 4.34 3.12 3.66 7.33
Lusaka 50 0.70 1.99 1.56 1.40 2.80
Streetfood E. coli count per neighborhood
neighb_UID city n min ecoli max ecoli mean ecoli standard deviation variance
201 Dhaka 10 0.69 5.50 4.81 5.08 10.16
202 Dhaka 10 2.32 5.83 5.14 5.34 10.68
203 Dhaka 10 2.01 6.44 5.67 5.99 11.97
204 Dhaka 10 1.67 5.94 5.12 5.52 11.03
205 Dhaka 10 1.22 4.48 3.88 4.04 8.08
206 Dhaka 10 2.52 5.62 4.97 5.22 10.45
207 Dhaka 10 2.04 4.45 3.76 3.98 7.96
208 Dhaka 10 3.53 5.79 5.04 5.32 10.64
209 Dhaka 10 1.80 5.27 4.80 4.93 9.85
210 Dhaka 10 2.27 5.80 5.26 5.37 10.74
401 Lusaka 20 0.70 1.99 1.59 1.53 3.06
601 Accra 10 2.00 6.55 5.64 6.05 12.09
602 Accra 10 1.95 6.37 5.37 5.87 11.73
701 Kumasi 10 1.32 3.16 2.63 2.76 5.51
702 Kumasi 10 1.84 2.30 2.06 1.66 3.33
703 Kumasi 10 1.79 4.34 3.65 3.94 7.87
704 Kumasi 10 1.83 2.97 2.29 2.42 4.84
901 Kampala 10 1.54 5.56 4.92 5.20 10.40
902 Kampala 9 1.91 5.31 4.75 4.90 9.80
903 Kampala 6 1.90 2.26 2.09 1.67 3.34
904 Kampala 10 1.71 3.85 3.34 3.42 6.85
905 Kampala 10 1.70 3.49 2.61 2.98 5.95
1201 Dakar 10 2.67 3.00 2.76 2.24 4.47
1202 Dakar 10 2.67 2.83 2.74 1.95 3.89
1203 Dakar 10 2.67 2.83 2.74 1.94 3.89
1204 Dakar 10 2.67 3.61 2.98 3.04 6.08
1205 Dakar 10 2.67 3.83 3.07 3.30 6.59
1301 Lusaka 10 1.12 1.99 1.61 1.40 2.80
1302 Lusaka 10 1.45 1.84 1.59 1.04 2.09
1303 Lusaka 10 0.98 1.74 1.44 1.14 2.29

Graphs - Differences within Produce Samples

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Drinking Water

Differences within Drinking Water

Municipal Drinking Water E. coli count per deployment
city n min ecoli max ecoli mean ecoli standard deviation variance
Accra 88 -0.3 3.30 1.86 2.49 4.98
Atlanta 10 -0.3 -0.30 -0.30 -Inf -Inf
Dakar 100 -0.3 0.00 -0.30 -1.30 -2.60
Dhaka 100 -0.3 4.76 3.36 3.88 7.75
Kampala 39 -0.3 1.11 -0.01 0.35 0.70
Kumasi 36 -0.3 3.22 1.71 2.44 4.88
Lusaka 40 -0.3 2.27 1.05 1.59 3.17
Maputo 40 -0.3 2.77 1.46 1.99 3.98
Siem Reap 10 -0.3 -0.30 -0.30 -Inf -Inf
Vellore 22 -0.3 2.30 1.23 1.71 3.42
Municipal Drinking Water E. coli count per neighborhood
neighb_UID city n min ecoli max ecoli mean ecoli standard deviation variance
104 Siem Reap 10 -0.30 -0.30 -0.30 -Inf -Inf
201 Dhaka 10 -0.30 1.38 0.70 0.90 1.79
202 Dhaka 10 -0.30 1.71 0.90 1.22 2.44
203 Dhaka 10 -0.30 1.35 0.64 0.86 1.71
204 Dhaka 10 -0.30 4.38 3.48 3.88 7.75
205 Dhaka 10 -0.30 2.18 1.19 1.68 3.36
206 Dhaka 10 -0.30 4.30 3.32 3.80 7.59
207 Dhaka 10 0.78 4.38 3.86 3.99 7.98
208 Dhaka 10 1.56 4.76 4.00 4.24 8.48
209 Dhaka 10 -0.30 2.62 2.18 2.18 4.36
210 Dhaka 10 -0.30 3.41 2.45 2.91 5.81
301 Accra 10 -0.30 1.38 0.95 1.01 2.03
302 Accra 30 -0.30 3.30 2.30 2.71 5.42
303 Accra 9 -0.30 0.00 -0.21 -0.66 -1.31
304 Accra 9 -0.30 0.48 -0.11 -0.08 -0.16
305 Accra 10 -0.30 -0.30 -0.30 -Inf -Inf
401 Lusaka 10 -0.30 0.30 -0.19 -0.32 -0.65
601 Accra 10 -0.30 1.91 1.00 1.40 2.81
602 Accra 10 -0.30 1.63 0.84 1.12 2.24
701 Kumasi 10 -0.30 1.49 1.08 1.02 2.03
702 Kumasi 10 -0.30 3.22 2.23 2.72 5.44
703 Kumasi 10 -0.30 0.30 -0.05 -0.34 -0.68
704 Kumasi 6 -0.30 1.40 0.66 1.00 2.00
801 Maputo 30 -0.30 2.77 1.53 2.05 4.09
802 Maputo 10 -0.30 1.72 1.10 1.26 2.52
902 Kampala 10 -0.30 -0.30 -0.30 -Inf -Inf
903 Kampala 9 -0.30 -0.30 -0.30 -Inf -Inf
904 Kampala 10 -0.30 -0.30 -0.30 -Inf -Inf
905 Kampala 10 -0.30 1.11 0.38 0.63 1.26
1001 Atlanta 10 -0.30 -0.30 -0.30 -Inf -Inf
1101 Vellore 11 -0.30 2.00 1.06 1.52 3.04
1102 Vellore 11 -0.30 2.30 1.36 1.82 3.65
1201 Dakar 20 -0.30 -0.30 -0.30 -Inf -Inf
1202 Dakar 20 -0.30 0.00 -0.28 -0.95 -1.90
1203 Dakar 20 -0.30 -0.30 -0.30 -Inf -Inf
1204 Dakar 20 -0.30 -0.30 -0.30 -Inf -Inf
1205 Dakar 20 -0.30 -0.30 -0.30 -Inf -Inf
1301 Lusaka 10 -0.30 -0.30 -0.30 -Inf -Inf
1302 Lusaka 10 -0.30 0.00 -0.26 -0.80 -1.60
1303 Lusaka 10 -0.30 2.27 1.64 1.85 3.69

Graphs - Differences within Drinking Water Samples

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Behavior - Comb

Adults - Combined Frequency

Yes/ No Questions

Children - Combined Frequency

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Exposure

Adults

The SaniPath exposure data is displayed here by pathway. The different colors represent the individual deployment, while a dot represents a particular neighborhood.

Children

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Map

Country Map

Neighborhood Map

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The end.